PERANCANGAN APLIKASI PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN METODE NAÏVE BAYES - Timotius, Adrian; Fenriana, Indah (ALGOR, 2024)
There is limited information on how to diagnose disease in the community, especially heart disease and a lack of awareness of the types of heart disease and related symptoms in predicting heart disease. The Naive Bayes algorithm is one of the algorithms that is often used in data mining to predict the likelihood of heart disease based on existing risk factors. The problem to be solved is how the diagnostic results are obtained from system design using the Naïve Bayes algorithm in diagnosing heart disease. This research technique uses the Naïve Bayes Algorithm. As for the method of data collection is the dataset and heritage studies. As for the results of his research, the naïve Bayes algorithm in the field of data mining computer science, especially in the classification of heart disease, is proven to be implemented properly. Tests carried out using the manual method and using the RapidMiner software produce accuracy as a measure of the accuracy of the algorithm in diagnosing heart disease. In addition, it also has a higher level of accuracy compared to the linear regression algorithm. the naïve Bayes method has an accuracy of 86.26%, while the linear regression algorithm only has an accuracy of 83%.